For as long as there have been classrooms, students have been trying to learn.
The success of their efforts has often been attributed to their teacher, their learning style or their affinity with the subject.
But how often do we attribute students' success or failure in the classroom to a computer program? This scenario is becoming increasingly likely, thanks to adaptive learning.
In one study of adaptive learning students achieved a 15% improvement in success rates. In another study failure rates in a course at the University of New South Wales reduced from 31% to 7%.
So what is adaptive learning and how is it different to computer-based learning we are already familiar with?
Adaptive learning uses computers as interactive teaching devices, which adapt to the unique needs of each learner. Learning material is presented to students according to their unique needs.
For example if a student is doing well, adaptive learning software could make the coursework more challenging while a different student using the same software may be presented with material better suited to their level of skill. The software might also adjust the type of instruction to better suit a person's learning style or preferences, even potentially their emotional state.
While traditional computer-based learning offers a fixed curriculum, adaptive learning provides a personalised learning experience, at scale. The number of students who can participate is limited only by access to a computer and the software.
Adaptive learning works by establishing at least three components: a model of the structure of the content that will be learned (a content model), a learner model, which is a means of understanding student abilities and finally an instructional model, which is a method of matching the content and its presentation to the student so that it is dynamic and personalised.
Big data also plays an important role, by increasing the power of algorithm-based systems to adapt more quickly.
If you think that adaptive learning systems sound too standardised to be truly sensitive to what and how students need to learn, adaptive learning has already evolved to address that. Some existing platforms provide authoring tools that enable individual teachers and lecturers to adjust courses and create their own.
Another potential limitation is the ability or inability of software to understand the subtleties and inflections present in longer strings of connected text. However natural language processing is already being used to enable systems to better interpret written responses.
What is truly fascinating about adaptive learning is it appears to be doing a better job than some teachers and lecturers, judging by the success rates reported in early studies. This does make you wonder what it will look like for teachers to work alongside adaptive learning technology in, say, a decade from now.
In 2013 the Bill and Melinda Gates Foundation published a white paper on adaptive learning that made the case for accelerating adaptive learning in higher education.
Another more immediate application with broader potential application is business education. It might not be too long before the cost of receiving an MBA, for example, is significantly reduced.
The future of work will require us all to have a more sophisticated skillset, so access to cheap, personalised learning is likely to continue to have a place.
About Nina Sochon
Nina Sochon is a leading expert on remote and flexible work in Australia and a High Performing Workplaces Consultant. Nina now assists businesses to drive amazing service outcomes and powerful staff engagement through a clear system of results management, effective leadership and highly productive remote and virtual work arrangements. Nina is CEO of Transformed Teams and a recognised High Performing Workplaces Expert.
